Author(s):
1. Sannaullah:
Department of Computing, Abasyn University Peshawar,,Pakistan
2. Syed Aizaz ul Haq:
Department of Computing, Abasyn University Peshawar,,Pakistan
3. Abdul Manan Ahmadzai:
Department of Computing, Abasyn University Peshawar,,Pakistan
4. M. Asif Sahim:
Department of Computing, Abasyn University Peshawar, Pakistan
Abstract:
Swarm intelligence, which is inspired by the group behavior of social insects and other animals, serves as the foundation for this study. A swarm is a collection of fish, birds, ants, bees, etc. Animal agents are employed in this swarm to maximize the issue. For large-scale optimization issues, where traditional mathematical techniques would not work, optimization algorithms have been industrialized to provide nearoptimal solutions. Algorithms for optimization are employed to minimize or maximize an objective function. Without any centralized information, each agent in the swarm operates independently, sharing knowledge and progressing together. Nevertheless, optimization techniques have their own set of drawbacks, which are mainly related to the restrictions of the objects they aim to simulate. Even though GSO is commonly utilized, it often experiences early convergence and may not have enough diversity in the glowworm population, which could hinder its capacity to travel away from local optima. The current project aims to address the GSO's shortcomings.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords:
Particle swarm optimization PSO
,
Particle swarm optimization PSO
,
Grey Wolf Optimization
,
Metaheuristic
,
Grey Wolf Optimization GSO
,
Optimization Algorithm
,
Glow worm swarm GSO
References:
References are not available for this document.
Citations
Citations are not available for this document.